学得更快，不是更紧：从 Reddit 和知乎提炼的高效学习线索
更快的学习通常不是靠更用力，而是靠更少的认知噪音、更好的理解压缩、更主动的提问与输出，以及把休息当成学习回路的一部分。
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Advanced Methodology
Better ways to learn, reason, experiment, and make decisions.
Good methods make difficult work easier to examine and improve. This collection focuses on learning systems, mental models, deliberate practice, and the feedback loops that turn ideas into reliable action.
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You Don't Need Certainty. You Need a Decision System
Most big decisions are not problems to solve but bets to place. A better framework: expand your option set, identify the uncertainties that actually matter, run cheap tests, prefer reversibility, and build adaptation into the decision itself.
Visualization Is Not About Winning. It Is About Rehearsal.
The useful form of visualization is not fantasy about the trophy but rehearsal of the process. Outcome fantasies can drain motivation; process rehearsal can reduce surprise, strengthen neural patterns, and make hard moments feel familiar enough to execute.
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PPO, One Question at a Time: From Rewards to Policy Updates
The questions behind my PPO notes: why rewards cannot simply backpropagate through sampled tokens, what advantage and GAE estimate, why on-policy rollouts still need probability ratios, and what clipping actually changes. Includes numerical examples, runnable gradient checks, and a focused reading path.
Agent Harness Engineering: Why I Recommend AI Agents in Depth
My recommended systems companion for becoming an LLM engineer: a guide to AI Agents in Depth, the model–harness boundary, context and tool design, outcome-based evaluation, and a practical reading path.
Learning RL from the Math: From Bellman Equations to LLM Post-Training
Why I strongly recommend Shiyu Zhao’s Mathematical Foundations of Reinforcement Learning, the questions that clarified RL for me, and a practical learning path from Bellman equations to LLM post-training.
Before I Trust the Model, I Audit the Data
How I think about noisy labels, data curation, and evaluation that deserves to be trusted, with lessons from MIT and three reproducible teaching notebooks.
Unraveling Life's Equation: Mathematical Thinking in Everyday Life 🌐🔢 (3min read)
Terence Tao talks about how mathematical think is related to every day life.